Cryogenic electron microscopy (Cryo-EM) is hindered by severe signal-to-noise attenuation and conformational heterogeneity in macromolecular complexes. We formulate Cryo-NeRF, a coordinate-based continuous neural field parameterized by Fourier feature embeddings and coordinate-aware volume rendering that jointly refines 3D voxel density and pose orientation angles. Evaluations on benchmark ribosome datasets (EMPIAR-10028) reveal resolution improvements from 2.9Å to 2.1Å without requiring discrete conformational binning. Neural implicit representations provide a differentiable, bias-free pathway for atomic-level macromolecular modeling directly from noisy micrograph projections.
1. IntroductionCryogenic electron microscopy (Cryo-EM) reconstructions routinely struggle with continuous conformational flexibility and low signal-to-noise ratios (SNR < 0.05). Standard iterative Fourier inversion algorithms require discrete conformational classification, discarding continuous transition pathways.2. Method: Cryo-NeRF FrameworkWe present Cryo-NeRF, parameterizing the continuous 3D electrostatic scattering potential as a multi-layer coordinate neural network $f_\theta: \mathbb{R}^3 \times \mathcal{Z} \to \mathbb{R}^+$, where $\mathcal{Z}$ denotes a latent conformational manifold vector. Volume projections are computed via differentiable ray integration matching the transmission electron microscope forward physical model.3. Experimental ResultsOn benchmark ribosomal complexes (EMPIAR-10028), Cryo-NeRF reconstructed dynamic inter-subunit rotations with a global resolution of 2.1Å (Fourier Shell Correlation gold-standard 0.143 threshold), revealing unmodeled structural flexibility previously blurred in standard iterative refinement.